wehub-resource-sync
116 lines
4.1 KiB
Plaintext
116 lines
4.1 KiB
Plaintext
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/rmsprop_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/impl/rmsprop_kernel_impl.h"
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namespace phi {
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template <typename T>
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struct RmsFunctor<T, GPUContext> {
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RmsFunctor(const GPUContext &dev_ctx,
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const DenseTensor ¶m,
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const DenseTensor &mean_square,
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const DenseTensor &grad,
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const DenseTensor &moment,
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const DenseTensor &learning_rate,
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const optional<DenseTensor> &mean_grad_opt,
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const optional<DenseTensor> &master_param,
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float epsilon_t,
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float decay_t,
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float momentum_t,
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bool centered,
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bool multi_precision,
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DenseTensor *param_out,
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DenseTensor *moment_out,
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DenseTensor *mean_square_out,
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DenseTensor *mean_grad_out,
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DenseTensor *master_param_outs) {
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auto &p_tensor = param;
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auto &ms_tensor = mean_square;
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auto &lr_tensor = learning_rate;
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auto &mom_tensor = moment;
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auto &grad_tensor = grad;
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size_t limit = static_cast<size_t>(ms_tensor.numel());
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DenseRmspropGradFunctor<T> grad_func(grad_tensor.data<T>());
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funcs::ForRange<GPUContext> for_range(dev_ctx, limit);
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using MT = typename MPTypeTrait<T>::Type;
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MT *master_out_data = multi_precision
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? dev_ctx.template Alloc<MT>(master_param_outs)
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: nullptr;
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if (centered) {
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auto mg_tensor = mean_grad_opt.get_ptr();
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if (mg_tensor) {
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PADDLE_ENFORCE_EQ(
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mg_tensor->Holder(),
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mean_grad_out->Holder(),
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common::errors::InvalidArgument(
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"MeanGrad and MeanGradOut must be the same Tensor"));
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} else {
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PADDLE_ENFORCE_EQ(
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mg_tensor,
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mean_grad_out,
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common::errors::InvalidArgument(
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"MeanGrad and MeanGradOut must be the same Tensor"));
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}
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for_range(CenteredRmspropFunctor<T, MT, DenseRmspropGradFunctor<T>>(
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dev_ctx.template Alloc<T>(param_out),
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dev_ctx.template Alloc<MT>(mean_square_out),
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dev_ctx.template Alloc<MT>(moment_out),
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dev_ctx.template Alloc<MT>(mean_grad_out),
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lr_tensor.data<MT>(),
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master_out_data,
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static_cast<MT>(decay_t),
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static_cast<MT>(epsilon_t),
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static_cast<MT>(momentum_t),
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grad_func));
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} else {
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for_range(UncenteredRmspropFunctor<T, MT, DenseRmspropGradFunctor<T>>(
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dev_ctx.template Alloc<T>(param_out),
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dev_ctx.template Alloc<MT>(mean_square_out),
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dev_ctx.template Alloc<MT>(moment_out),
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lr_tensor.data<MT>(),
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master_out_data,
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static_cast<MT>(decay_t),
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static_cast<MT>(epsilon_t),
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static_cast<MT>(momentum_t),
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grad_func));
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}
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}
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};
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template struct RmsFunctor<GPUContext, float>;
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template struct RmsFunctor<GPUContext, double>;
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template struct RmsFunctor<GPUContext, float16>;
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} // namespace phi
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PD_REGISTER_KERNEL(rmsprop,
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GPU,
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ALL_LAYOUT,
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phi::RmspropDenseKernel,
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float,
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double,
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phi::float16) {}
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PD_REGISTER_KERNEL(rmsprop_dense_param_sparse_grad,
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GPU,
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ALL_LAYOUT,
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phi::RmspropSparseKernel,
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float,
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double,
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phi::float16) {}
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